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French National Research Institute for Sustainable Development | Montpellier, Languedoc Roussillon | France | 22 days ago
project is hosted, focuses on data sciences, remote sensing, climate and environmental research. Heat extremes are among the deadliest meteorological phenomena, and their intensity and frequency
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/autumn floods. The interdisciplinary SUPRAGRASS project addresses these challenges by developing operational tools based on remote sensing, ecosystem simulation, and stakeholder engagement. The aim is to
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that are derived from novel ground-based atmospheric remote sensing profile observations. Heat risks to human health and mortality are exacerbated in urban areas, where heat waves are more intense and last longer
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, and will work in close collaboration with the FOX team of the CRISTAL laboratory. The LOA team has internationally recognized expertise in the field of radiative transfer and remote sensing. The CRISTAL
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algorithm development and satellite remote sensing • Good written and spoken English • Ability to work independently as well as in a team • Proficiency in programming languages (e.g. Python, R, Fortran
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Eligibility criteria . PhD in atmospheric sciences • Knowledge of cloud physics • Experience in algorithm development and satellite remote sensing • Good written and spoken English • Ability to work
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the broad sense. We are recruiting a Post-doctoral researcher invited to propose a research project on the computer processing of sound and musical data within the Studio for Creation and Research in
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proposes to combine in situ and remote sensing data, radiative transfer models, and chemical and physical analytical methods to better understand the fundamental properties and the impact of mineral dust
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for calibrating remote sensing instruments and validating national and regional aerial biomass maps. The One Forest Vision initiative (OFVi project, https://www.oneforestvision.org/eng ) has enabled the creation
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validated on challenging AI applications, such as the separation of different brain processes in functional magnetic resonance data in a federated learning setting [8], or cross-scene hyperspectral/remote